Smart Healthcare
At Sapio, Our Vision Is To Enable Policy Formulators And Stakeholders To Use Intelligence Of Data Thereby, Following The Three Mantras Of
All leading to complex decision making everyday
Cost of each decision is too high
Impacting lives of patients Revenue of hospital Healthcare & Governance Ecosystem
Sapio Smart Healthcare is committed towards solving the menace of COVID-19 through its innovative solutions. For the medical world, it has created Protocol Bots to help doctors and other healthcare professionals, by guiding them on the right steps for treatment.
For the citizens, it has created an ecosystem to find resources needed to help them during COVID, and seek resources from other citizens who can provide the same. It is now being planned to be executed through a chat bot and will have the support of governments.
Integration of these tools and adding a layer of smartness for the analytics driven out of it remains the weakness.
Therefore, enabling the need for a smart data backed Decision Support System which can ensure that the complexities of the network become the strength instead of weakness.
Enhancing
Enhancing the operations of hospital by making them ‘smart’
Supporting
Supporting the government to make policies on a hyperlocalised level which can further enhance the healthcare infrastructure on a macro level
Integrating all the basic / traditional systems so that they can start talking to each other in a manner such that useful outputs can be derived out of it. The smart elements can further extract the value of the data being already captured, providing insights which cannot be seen using traditional systems alone.
Sapio – being a government advisory firm, has been able to provide successful policy
recommendations to multiple state and central government agencies. We tend to
leverage the hyperlocal data points that we have collected through our extensive primary
and secondary research.
- Demographic Details
- Social Media Analytics
- Patient Lifecycle (assuming to come from Objective 1)
- Consumer Behaviour
- Income & Expenditure Distribution
- Socio-Economic Distribution
- Importance of Health (perception)
- Digital Penetration
- Activity & Sector Correlation
- Geographic Distribution
- Community Distribution
- Effectiveness of Government Schemes
Enabling
- Patient Life Cycle Analytics
- Resource Optimization
- Predictive Analytics & Diagnosis
Therefore
- Cost Efficient Healthcare Infrastructure
- High Quality Patient Treatment
VALUE ADDED TOOLS FOR ‘SMARTNESS'
- Robotic Process Automations
- Physician Recommendation Bot Tool
- Clerical Bot to handle all ERP works for Hospital management
- Advance Analytic Chat Bots
- OCR and Speech to Text Tools to ease the works of the doctors
Urban Centres/ Smart Cities
Rural Centres
Research & Development
Smart Hospital Ecosystem
Target hospital: Government / Municipal Hospitals
There are multiple government and municipal hospitals in any urban centre
A large number of data is being collected (yet, at some places digitisation of such data remains a challenge)
Thereby, introducing the concept to ‘Smart Hospital’ (meant for supporting all stakeholders of a hospital), which can be ensured with the following steps:
- Data collection and digitisation – which can be achieved through primary as well as extensive scientific secondary research.
- Creation of basic IT layers for a hospital (for new).
- Injection of Smart Elements (AI) into the basic layers (for existing).
- Adding a layer of Smartness & required Analytics.
Citizen Engagement: Patients’ portals allow the focus to be on citizens while designing the smart layers.
Phase I – selection of single hospital for pilot purpose.
Phase II – enhancement and interdepartmental interlinkages for enhanced and optimized hospital operations and patient lifecycle.
Phase III – scaling the existing model to ‘Ward’ level (after successful completion of trial).
Phase IV – ward level interlinkages which can enable a smart policy up to hyperlocal level. This can further be enhanced with the help of our existing ‘Hyperlocal Consumer Behaviour Identifier Tool’. Interconnected modules with interconnected hospitals but in a single ward can lead to generation of important and interesting insights which can help revolutionize the healthcare infrastructure up to the ward level.
Phase V – pan city scaling of the model for city level analysis. Phase
Phase VI – conversion of all existing government hospitals into Smart Hospitals, evaluation and certification of existing private hospitals on level of smartness, and new templates for creation of new hospitals.
Rural Centres
Target: Primary Health Care Centres
There are multiple small centres in rural areas, but their effectiveness needs to be increased multiple times, by using digitisation technologies
Many new centres can be created with very limited workforce and budget using the concept of smart hospitals
Localised rural-level research and development with an evaluation of preventive and curative methods can help improve efficiency of the healthcare system
Phase I – Identification of Rural areas that need special focus
- Data collection – through primary and secondary survey, we can collect the required demographic and geographical data for the rural area
- Hyperlocal GIS mapping up to the gram panchayat level can be made for better visualization. This mapping shall include all the healthcare centres of the rural village including the National Health Mission Centres.
- GIS mapping to showcase availability of medical infrastructure and facilities.
Phase II – Resource optimization in the Rural Areas
- Cluster formation – this can enable to extrapolate the data from the pilot villages to other villages based on same demographic and geographical details, and use it to better optimise resources
- Primary Healthcare Centre Planning & Decision Making– Plans on how and where to have healthcare centres or what specific improvements needed in an existing one
- GIS mapping to showcase availability of medical infrastructure and facilities.
Phase III – Making Centres “Smart”
- Applying the concept of “smart hospitals” on these centres in a customised way
Phase IV – Creating New A.I. driven Primary Centres or Hybrid Primary Centres (based on extent of digitisation in rural areas)
- Scale up creation of new centres with limited workforce
- Use of General Physician Bots & related technologies
Research & Development
- Localised Research and Identification of Patterns in diseases and conditions.
- Predictive Modelling for disease or condition.
- Action Items Recommendation for specific localities.
- Citizen Engagement to enhance the power of research.
- Use of Our Citizen Focused Tools to improve outputs.
- Feedback loop to continuously come up with new research.
- Based on localised research, resource optimization can be improved. Also, the citizens can be warned accordingly.
- Global Research, Big Data for combined research.
- Use of data points across localities.
- More globalised research helps in better preparation for situations like COVID-19.
